Malicious traffic detection often requires large, labeled datasets, which are challenging due to privacy concerns, labeling costs, and evolving threat patterns. Although recent self-supervised pretraining methods address this issue, they rely on complex transformer-based architectures that are computationally expensive and have high inference times, making them unsuitable for real-time use. In addition, most existing approaches process packets or flows independently, and often rely on per-packet dataset splits that introduce implicit flow-level data leakage, thereby limiting their ability to capture meaningful semantic and behavioral relationships across flows for detecting stealthy encrypted threats. To address these issues, we propose LitCVit, a lightweight self-supervised contrastive Vision Transformer-based framework that captures cross-flow semantic and behavioral patterns to generate robust latent representations of encrypted traffic. Without relying on decryption or manually engineered features, our method enables efficient detection of encrypted malicious flows with low inference time. Extensive evaluations on benchmark datasets demonstrate that the proposed framework achieves an average detection accuracy of 98.10% and F1-score of 98.08%. Compared to the best state-of-the-art model, LitCVit achieves an average improvement of 2.49% in F1-score, 2.12% in precision, and 2.50% in recall, highlighting its superior detection capability in encrypted traffic scenarios. Additionally, LitCVit achieves an 8.7× reduction in inference time compared to the best existing self-supervised approach, making it highly suitable for deployment on resource-constrained devices.

The desire to overcome reliability issues of the distributed energy resources (DERs) led researchers to develop a novel concept named virtual power plant (VPP). VPPs are supposed to carry out intelligent and secure energy trading among prosumers, buyers, and generating stations along with providing efficient energy management. Therefore, integrating blockchain within a decentralized VPP network emerged as a novel paradigm. However, this decentralization also suffers with trust, reliability, energy management, and efficiency issues due to DERs dynamic nature. Thus, in this article, we first work to provide an efficient energy management strategy for VPPs to enhance demand response, then we propose an energy oriented trading and block mining protocol and name it as P roof o f E nergy M arket (PoEM). To enhance it further, we integrate differential privacy in PoEM and propose a P rivate PoEM (PPoEM) model. Collectively, we propose a private decentralized VPP trading model and named it as V irtual P rivate T rading (VPT). We further carry out extensive theoretical analysis and derive step-by-step valuations for market race probability, market stability probability, energy trading expectation, winning state probability, and prospective leading-time profit values. Afterwards, we carry out simulation-based experiments of our proposed model to show the effectiveness and novelty as compared to state-of-the-art works.

Differential private (DP) query and response mechanisms have been widely adopted in various applications based on Internet of Things (IoT) to leverage variety of benefits through data analysis. The protection of sensitive information is achieved through the addition of noise into the query response which hides the individual records in a dataset. However, the noise addition negatively impacts the accuracy which gives rise to privacy-utility tradeoff. Moreover, the DP budget or cost$\epsilon $is often fixed and it accumulates due to the sequential composition which limits the number of queries. Therefore, in this article, we propose a framework known as optimized privacy-utility tradeoff framework for data sharing in IoT (OPU-TF-IoT). First, OPU-TF-IoT uses an adaptive approach to utilize the DP budget$\epsilon $by considering a new metric of population or dataset size along with the query. Second, our proposed heuristic search algorithm (HSA) reduces the DP budget accordingly whereas satisfying both data owner and data user. Third, to make the utilization of DP budget transparent to the data owners, a blockchain-based verification mechanism is also proposed. Finally, the proposed framework is evaluated using real-world datasets and compared with the traditional DP model and other related state-of-the-art works. The results demonstrate that our proposed framework not only utilizes the DP budget$\epsilon $efficiently, but also optimizes the number of queries by 49% and 54% on average compared to state-of-the-art and standard DP models, respectively. Furthermore, the data owners can effectively make sure that their data are shared accordingly through our blockchain-based verification mechanism which encourages them to share their data into the IoT system.

Terahertz (THz) wireless links have been suggested for wireless data centres for their efficiency, high data rates upto terabits per second, and low latency compared to wired links. However, THz band has extremely high path loss due to which directional antennas (DA) are used. In DAs usage, beamwidth plays an important role in improving the communication establishment and efficiency. More precisely, narrow beam offers more communication distance and robust signal strength whereas wide beamwidth offers high throughput but covers less communication distance and can improve the overall link discovery time. Thus, to ensure reliable communication, the impact of beamwidth needs to be studied in THz MAC protocol, as it directly influences link discovery, throughput and packet loss. In this paper, we perform extensive analysis to understand the effect of antenna beamwidth on the performance of THz MAC protocols in the context of the data center (DC). We used NS3 THz module for the simulation, and our results showed that there are trade-offs involved in the performance of THz MAC protocol due to beamwidth. These trade-offs will provide the basis to develop and design a new THz MAC protocol by taking into consideration the impact of beamwidth and being helpful for optimal selection of beamwidth.

Terahertz (THz) wireless communication is a promising solution aimed to solve the problems of wired data centers (DCs). THz has the capability of providing ultra-high data rate up to terabit-per-second ($T$b/s) with low latency communication in μs. Additionally, it offers flexibility, efficiency, scalability, and it has a high resource utilization compared to wired links. However, despite these advantages, THz suffers from high path loss because of which directional antennas (DAs) are used. These DAs creates deafness issues for which rotating DAs are used at both ends of transmitter and receiver nodes. These rotating DAs further bring about a synchronization problem at the link layer for Medium Access Control (MAC) protocol. In this regard for timely coordination between the nodes, receiver-initiated THz MAC protocols have been proposed. However, these protocols have used fixed beamwidth which provides high throughput when using wider beams but at the cost of lower range leading to less node's discovery in the DC topology. Hence to address this problem, we propose a receiver-initiated THz MAC protocol named as Dynamic Beamwidth Selection based THz MAC protocol (DBS-ADAPT). Our protocol selects wider beamwidth during the data transmission phase while maintaining the link connectivity between the nodes through exchange of node position/distance which ensures communication range is not compromised while using wider beamwidth. We evaluate the performance of our proposed protocol using NS3 THz network simulator Terasim. Results show that our protocol outperforms the ADAPT-3 THz MAC protocol in terms of throughput and packet time.

The integration of permissioned blockchain such as Hyperledger fabric (HF) and Industrial internet of Things (IIoT) has opened new opportunities for interdependent supply chain partners to improve their performance through data sharing and coordination. The multichannel mechanism, private data collection and querying mechanism of HF enable private data sharing, transparency, traceability, and verification across the supply chain. However, the existing querying mechanism of HF needs further improvement for statistical data sharing because the query is evaluated on the original data recorded on the ledger. As a result, it gives rise to privacy issues such as leaking of business secrets, tracking of resources and assets, and disclosing of personal information. Therefore, we solve this problem by proposing a differentially private enhanced permissioned blockchain for private data sharing in the context of supply chain in IIoT which is known as (EDH-IIoT). First, we integrate differential privacy into the chaincode (smart contract) of HF which evaluates the query and adds a calibrated noise into it. Second, we propose an algorithm to efficiently utilize ϵ through reuse of the privacy budget for the repeated queries. Third, we also propose an algorithm to track the privacy budget (ϵ) and avoid the degrade of privacy preservation in case of multiple queries on the same portion of the ledger's data. Furthermore, the reuse and tracking of ϵ enables the data owner to ensure that ϵ does not exceed the threshold which is the maximum privacy budget (ϵt). Finally, we model two privacy attacks namely linking attack and composition attack to evaluate and compare privacy preservation, and the efficiency of reuse of ϵ with the default chaincode of HF and traditional differential privacy model, respectively. The results confirm that EDH-IIoT obtains an accuracy of 97% in the shared data for ϵ = 1, and a reduction of 35.96% in spending of ϵ.

In order to efficiently provide demand side management (DSM) in smart grid, carrying out pricing on the basis of real-time energy usage is considered to be the most vital tool because it is directly linked with the finances associated with smart meters. Hence, every smart meter user wants to pay the minimum possible amount along with getting maximum benefits. In this context, usage based dynamic pricing strategies of DSM plays their role and provide users with specific incentives that help shaping their load curve according to the forecasted load. However, these reported real-time values can leak privacy of smart meter users, which can lead to serious consequences such as spying, etc. Moreover, most dynamic pricing algorithms charge all users equally irrespective of their contribution in causing peak factor. Therefore, in this paper, we propose a modified usage based dynamic pricing mechanism that only charges the users responsible for causing peak factor. We further integrate the concept of differential privacy to protect the privacy of real-time smart metering data. To calculate accurate billing, we also propose a noise adjustment method. Finally, we proposeDemandResponse enhancingDifferentialPricing (DRDP) strategy that effectively enhances demand response along with providing dynamic pricing to smart meter users. We also carry out theoretical analysis for differential privacy guarantees and for cooperative state probability to analyze behavior of cooperative smart meters. The performance evaluation of DRDP strategy at various privacy parameters show that the proposed strategy outperforms previous mechanisms in terms of dynamic pricing and privacy preservation.11.A preliminary version has been published by 2020 IEEE International Conference on Communications (ICC 2020), June, 2020, Dublin, Ireland entitled Differentially Private Dynamic Pricing for Efficient Demand Response in Smart Grid.

In this paper, we propose to use deep reinforcement learning (DRL) for the task of cooperative spectrum sensing (CSS) in a cognitive radio network. We selected a recently proposed offline DRL method called conservative Q-learning (CQL) due to its ability to learn complex data distributions efficiently. The task of CSS is performed as follows. Each secondary user (SU) performs local sensing and using CQL algorithm, determines the presence of licensed user for current and$k$-1 future timeslots. These results are forwarded to the fusion centre where another CQL algorithm is operating that generates a global decision for the current and$k$-1 future timeslots. Then, SUs do not perform sensing for the next$k$-1 timeslots to save energy. The proposed CSS mechanism can significantly increase the licensed user detection accuracy and the data transmissions by SUs. In addition, it reduces the sensing results transmission overhead. The proposed solution is tested with a stochastic traffic load model for different activity patterns. Our simulation results show that the proposed problem formulation using the CQL algorithm can achieve similar detection accuracy as other state-of-the-art methods for CSS while significantly reducing the computation time.

Efficient load utilization in order to match electric supply is one of the most considered factors in smart grid management. Demand side management (DSM) strategies such as real-time dynamic energy pricing has motivated customers to efficiently use their energy in order to reduce their bills intelligently. However, this real-time dynamic pricing can be a threat to privacy of smart homes inhabitants, as their lifestyle can easily be revealed via these real-time electricity values. Therefore, a strong privacy preserving strategy needs to be incorporated with real-time pricing. In this paper, we propose a Differentially private demand Response enhancing Dynamic Pricing (DRDP) strategy that incorporates the advantages of differential privacy, and usage based dynamic billing. The proposed strategy effectively protects user's privacy along with enhancing dynamic pricing by incentivizing the participating smart homes.

In this paper, we investigate the task of quality of service (QoS) routing in software defined networks (SDN). We consider delay, bandwidth, loss, and cost as QoS parameters. We propose a new deep reinforcement learning solution for greedy online QoS routing in SDN and call it Deep Q-Routing (DQR). DQR utilises a dueling deep Q-network with prioritised experience replay to compute a path for any source-destination pair request in the presence of multiple QoS metrics. In contrast to existing DRL-based routing methods, the proposed DQR method regards the task of routing as a discrete control problem and uses a reward function comprising weighted QoS parameters. Our simulation results show that DQR substantially improves end-to-end throughput compared to other existing learning based methods.

This paper investigates the task how to achieve fairness in distributed dynamic spectrum access (DSA). Specifically, we consider a cognitive radio network scenario with multiple primary users (PUs) and secondary users (SUs). Each PU operates in a licensed channel. We assume that there is no coordination between PUs and SUs, and no coordination among SUs. The key challenges for SUs are to: (1) avoid collisions with PUs, (2) avoid collisions with other SUs, (3) fair access of spectrum resources in an uncoordinated system, (4) deal with different PU activity patterns, (5) deal with spectrum sensing errors. To address these challenges, we propose a deep reinforcement learning (DRL) approach and an associated reward function to achieve fair access to spectrum resources. Specifically, we use the method of Dueling Double Deep Q-Networks with Prioritised Experience Replay (D3QN-PER) as DRL algorithm for each SU. In our simulation experiments, we demonstrate that the proposed approach performs better than existing DRL methods.

Modern smart homes are being equipped with certain renewable energy resources that can produce their own electric energy. From time to time, these smart homes or microgrids are also capable of supplying energy to other houses, buildings, or energy grid in the time of available self-produced renewable energy. Therefore, researches have been carried out to develop optimal trading strategies, and many recent technologies are also being used in combination with microgrids. One such technology is blockchain, which works over decentralized distributed ledger. In this paper, we develop a blockchain based approach for microgrid energy auction. To make this auction more secure and private, we use differential privacy technique, which ensures that no adversary will be able to infer private information of any participant with confidence. Furthermore, to reduce computational complexity at every trading node, we use consortium blockchain, in which selected nodes are given authority to add a new block in the blockchain. Finally, we develop differentially private Energy Auction for bLockchain-based microgrid systems (DEAL). We compare DEAL with Vickrey-Clarke-Groves (VCG) auction scenario and experimental results demonstrates that DEAL outperforms VCG mechanism by maximizing sellers' revenue along with maintaining overall network benefit and social welfare.

Information-centric networking (ICN) is being realized as a promising approach to accomplish the shortcomings of current Internet protocol-address-based networking. ICN models are based on naming the content to get rid of address-space scarcity, accessing the content via name-based-routing, and caching the content at intermediate nodes to provide reliable, efficient data delivery, and self-certifying contents to ensure better security. Obvious benefits of ICN in terms of fast and efficient data delivery and improved reliability raises ICN as highly promising networking model for Internet of Things (IoT) like environments. IoT aims to connect anyone and/or anything at any time by any path on any place. From last decade, IoT attracts both industry and research communities. IoT is an emerging research field and still in its infancy. Thus, this paper presents the potential of ICN for IoT by providing state-of-the-art literature survey. We discuss briefly the feasibility of ICN features and their models (and architectures) in the context of IoT. Subsequently, we present a comprehensive survey on ICN-based caching, naming, security, and mobility approaches for IoT with appropriate classification. Furthermore, we present operating systems and simulation tools for ICN-IoT. Finally, we provide important research challenges and issues faced by ICN for IoT.

Rising energy costs, losses in the present-day electricity grid, risks from nuclear power generation, and global environmental changes are motivating a transformation of the conventional ways of generating electricity. Globally, there is a desire to rely more on renewable energy resources (RERs) for electricity generation. RERs reduce greenhouse gas emissions and may have economic benefits, e.g., through applying demand side management with dynamic pricing so as to shift loads from fossil fuel-based generators to RERs. The electricity grid is presently evolving toward an intelligent grid, the so-called smart grid (SG). One of the major goals of the future SG is to move toward 100% electricity generation from RERs, i.e., toward a 100% renewable grid. However, the disparate, intermittent, and typically widely geographically distributed nature of RERs complicates the integration of RERs into the SG. Moreover, individual RERs have generally lower capacity than conventional fossil fuel-based plants, and these RERs are based on a wide spectrum of different technologies. In this article, we give an overview of recent efforts that aim to integrate RERs into the SG. We outline the integration of RERs into the SG along with their supporting communication networks. We also discuss ongoing projects that seek to integrate RERs into the SG around the globe. Finally, we outline future research directions on integrating RERs into the SG.

The papers in this special section focus on the deployment of information and communication technology (ICT) in smart grids as it relates to renewable energy resource management. The successful integration of renewable energy into the power grid is expected to reduce the dependence of the grid on the fossil fuels. The potential renewable energy resources include light, wind, vibration, heat, biofuel, biomass, and tides. It is envisaged that the use of renewable energy will reduce the use of traditional energy resources, such as nuclear, oil, and gas, in the future and this trend will continue in order to reduce the emission of greenhouse gases. The abundance of these renewable distributed energy resources (DERs) at the consumer side may help to develop distributed renewable energy generation at a large scale. The DERs will likely be an integral part of the future electric grid, i.e., the smart grid [4]–[6]. A prominent feature of the smart grid is that it allows for two-way communication between the utility and its customers through ICTs.

Internet of Things (IoT) is a world wide network of interconnected objects. IoT capable objects will be interconnected through wired and wireless communication technologies. However, cost-effectiveness issues and accessibility to remote users make wireless communication as a feasible solution. A majority of possibilities have been proposed but many of these suffer from vulnerabilities to dynamic environmental conditions, ease of access, bandwidth allocation and utilization, and cost to purchase spectrum. Thus trends are shifting to the adaptability of Cognitive Radio Networks (CRNs) into IoT. Additionally, ubiquitous objects with cognitive capabilities will be able to make intelligent decisions to achieve interference-free and on-demand services. The main goal of this paper is to discuss how CR technology can be helpful for the IoT paradigm. More precisely, in this paper, we highlight CR functionalities, specially spectrum sensing in conjunction with cloud services to serve as self-reconfigurable IoT solutions for a number of applications.

We plan to design and implement software defined intelligent antenna switching capability to wireless sensor nodes based on link quality metric, such as Received Signal Strength Indicator (RSSI). In this paper, as a first step, we discuss the preliminary results of our newly designed radio module (Inverted-F Antenna) for 2.4 GHz bandwidth wireless sensor networks. In this perspective, we consider the TelosB motes and compare the performance of the built-in TelosB antenna with our proposed antenna. Experimental results confirm the effectiveness of the proposed radio module i.e., 5% to 12% gain over the built-in radio module of the TelosB motes.

Channel selection plays a vital role in efficient and reliable data dissemination. In the context of Cognitive Radio Network (CRN), channel selection is more challenging due to the traffic pattern and channels' occupancy of primary radio (PR) nodes. Moreover, Cognitive Radio (CR) transmissions should not degrade the reception quality of PR nodes and should be immediately interrupted whenever a neighboring PR activity is detected. Thus, it is essential and, however, extremely challenging for CR nodes to correctly select channels allowing reliable communication.